#!/usr/bin/python
# -*- coding: UTF-8 -*-

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size,out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1,)
    Wx_plus_b = tf.matmul(inputs,Weights) + biases

    if activation_function is None :
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)

    return outputs

def compute_accuracy(v_xs,v_ys):
    global prediction
    y_pre = sess.run(prediction,feed_dict={xs:v_xs})        # 预测值
    correct_prediction = tf.equal(tf.argmax(y_pre,1),tf.argmax(v_ys,1))     # 比较预测值和实际值是否相等
    accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
    result = sess.run(accuracy,feed_dict={xs:v_xs,ys:v_ys})
    return result

# 定义输入数据placeholder
xs = tf.placeholder(tf.float32, [None,784])      # 不指定训练集图片个数，每个数据图片有28*28 = 784个点
ys = tf.placeholder(tf.float32, [None,10])

# 定义输出layer
prediction = add_layer(xs,784,10, activation_function=tf.nn.softmax)


# prediction与正确值的误差
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys*tf.log(prediction),reduction_indices=[1]))     # 决策树算法?

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(50000):
    batch_xs,batch_ys = mnist.train.next_batch(500)
    sess.run(train_step,feed_dict={xs:batch_xs,ys:batch_ys})
    if i % 50 == 0:
        print(compute_accuracy(mnist.test.images,mnist.test.labels))
